Yuchen Li

The paper proposes AdaBFL, a multi-layer defensive adaptive aggregation method that enhances Byzantine-robust federated learning by adaptively adjusting defense weights to counter complex poisoning attacks.

The paper proposes MaskDiff-AD, a forward-only masked diffusion model trained on nominal data to achieve state-of-the-art anomaly detection across various categorical, mixed-type, and text datasets.

The paper introduces Graph-Distance Contribution Reward (GDCR) and Step Advantage Policy Optimization (SAPO) to provide fine-grained, step-level credit assignment for agentic search by modeling world knowledge as a latent graph.

The JAMEL framework addresses the challenge of effective exploration in open-ended environments by jointly training agent memory and exploration policies using natural, novelty-driven signals.

This paper proposes SHA-PF, a search hardness-aware LLM-based problem formulation framework for expensive simulation-driven design, which prioritizes rare samples with greater progress potential and requires significantly fewer evaluations to reach design requirements.

This paper introduces X-Stage, a software-visible post-issue pipeline stage to improve communication efficiency in distributed diffusion transformer (DiT) inference, leading to significant speedups for DeepGEMM MegaMoE and Ulysses sequence-parallel attention.